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   "cells": [
    {
     "cell_type": "markdown",
     "id": "A3C2B1D537BC4D1685A6C048CF0A158B",
     "metadata": {},
     "source": [
      "# **\u76ee\u5f55**\n",
      "---\n",
      "1. \u7b80\u4ecb\n",
      "2. API\n",
      "3. \u793a\u4f8b"
     ]
    },
    {
     "cell_type": "markdown",
     "id": "A3392A580CCF4B1B8978CFE8DB0F9B35",
     "metadata": {},
     "source": [
      "# **1. \u7b80\u4ecb**\n",
      "---\n",
      "\u5728\u591a\u56e0\u5b50\u9009\u80a1\u6a21\u578b\u5f53\u4e2d\uff0c\u5e38\u89c1\u7684\u7ec4\u5408\u6784\u5efa\u7684\u65b9\u5f0f\u4e3a\u6392\u5e8f\u6cd5\u548c\u7b5b\u9009\u6cd5\u3002\u6392\u5e8f\u6cd5\u9009\u51fa\u7b2c\u51e0\u5206\u4f4d\u7ec4\u7684\u80a1\u7968\uff0c\u7136\u540e\u8fdb\u884c\u7b80\u5355\u7684\u7b49\u6743\u6216\u8005\u5e02\u503c\u52a0\u6743\uff0c\u7b5b\u9009\u6cd5\u4f1a\u5148\u5bf9\u80a1\u7968\u8fdb\u884c\u5206\u5c42\uff0c\u5728\u6bcf\u4e00\u5c42\u91cc\u9009\u51fa\u5bf9\u5e94\u7684\u7b2c\u51e0\u5206\u4f4d\u7ec4\u7684\u80a1\u7968\u3002\u7136\u800c\u8fd9\u4e24\u79cd\u65b9\u6cd5\u5728\u7ec4\u5408\u98ce\u9669\u7684\u63a7\u5236\u4e0a\u5e76\u4e0d\u591f\u7cbe\u7ec6\uff0c\u56e0\u6b64\u9700\u8981\u4f7f\u7528\u7ec4\u5408\u4f18\u5316\u7684\u7b97\u6cd5\u6765\u7ed9\u51fa\u4e2a\u80a1\u7684\u6743\u91cd\u3002\u4f18\u77ff\u7684\u4f18\u5316\u5668\u6b63\u662f\u4e3a\u4e86\u5b9e\u73b0\u8fd9\u4e00\u529f\u80fd\u800c\u63a8\u51fa\u7684\u3002\n",
      "\u4f18\u5316\u5668\u7684\u6a21\u578b\u5927\u6982\u53ef\u4ee5\u7528\u4e0b\u9762\u7684\u6570\u5b66\u5f0f\u5b50\u8fdb\u884c\u8868\u793a\uff1a\n",
      "$$max:\\omega'\\alpha$$\n",
      "$$st:min\\ weight<=\\omega_{i,t}<=max\\ weight$$\n",
      "$$\\sum_{k}\\omega_{i,t}=1$$\n",
      "$$\\sum_{k}|\\omega_{i,t}-\\omega_{i, t-1}| <= turnover\\ target$$\n",
      "$$x_{style,lower}<=X_{style}\\omega<=x_{style,upper}$$\n",
      "$$x_{indu,lower}<=X_{indu}\\omega<=x_{indu,upper}$$\n",
      "$$0 <= \\omega'\\Sigma\\omega <= risk$$\n",
      "$$0 <= (\\omega - \\omega_b)'\\Sigma(\\omega - \\omega_b) <= tracking\\ error$$\n",
      "`\u6211\u4eec\u505a\u4e86\u4e9b\u4fee\u6539\uff0c\u5c06\u539f\u5148\u76ee\u6807\u51fd\u6570\u7684\u4e8c\u6b21\u9879\u653e\u5728\u7ea6\u675f\u5f53\u4e2d\uff0c\u7701\u53bb\u4e86\u5bf9\u98ce\u9669\u538c\u6076\u7cfb\u6570\u7684\u4f30\u8ba1`"
     ]
    },
    {
     "cell_type": "markdown",
     "id": "BA9883D8F5D74F9D873FE9ABEC2EDCF1",
     "metadata": {},
     "source": [
      "\u4e0a\u9762\u7684\u516c\u5f0f\u7ed9\u4e86\u4e03\u4e2a\u7ea6\u675f\uff1a\n",
      "* `\u4e2a\u80a1\u6743\u91cd\u7ea6\u675f`:\u8fd9\u662f\u4e00\u4e2a\u5f88\u660e\u663e\u7684\u7ea6\u675f\uff0c\u56e0\u4e3aA\u80a1\u5e02\u573a\u4e0d\u5141\u8bb8\u505a\u7a7a\uff0c\u6240\u4ee5\u6211\u4eec\u4e2a\u80a1\u7684\u6743\u91cd\u5fc5\u987b\u8981\u63a7\u5236\u57280\u52301\u4e4b\u95f4\n",
      "* `\u6743\u91cd\u4e4b\u548c\u7ea6\u675f`:\u8fd9\u4e2a\u7ea6\u675f\u662f\u5f3a\u5236\u7684\uff0c\u5b83\u4f1a\u4f7f\u5f97\u6211\u4eec\u7ec4\u5408\u6700\u7ec8\u7684\u6743\u91cd\u4e4b\u548c\u4e3a1\uff0c\u5373\u5168\u989d\u6295\u8d44\n",
      "* `\u6362\u624b\u7387\u7ea6\u675f`:\u6211\u4eec\u5bf9\u6362\u624b\u7387\u8fdb\u884c\u63a7\u5236\uff0c\u6362\u624b\u7387\u7684\u8ba1\u7b97\u516c\u5f0f\u4e3a\uff1a$\\sum_{k}|\\omega_{i,t}-\\omega_{i, t-1}|$\uff0c\u6ce8\u610f\u8fd9\u4e2a\u7ea6\u675f\u5e26\u7edd\u5bf9\u503c\uff0c\u6c42\u89e3\u8d77\u6765\u8981\u7528\u7279\u6b8a\u7684\u65b9\u6cd5\n",
      "* `\u98ce\u683c\u56e0\u5b50\u7ea6\u675f`\uff1a\u6211\u4eec\u8981\u63a7\u5236\u7ec4\u5408\u5728\u67d0\u4e9b\u98ce\u683c\u56e0\u5b50\u4e0a\u7684\u66b4\u9732\n",
      "* `\u884c\u4e1a\u56e0\u5b50\u7ea6\u675f`\uff1a\u6211\u4eec\u8981\u63a7\u5236\u7ec4\u5408\u5728\u884c\u4e1a\u4e0a\u7684\u66b4\u9732\n",
      "* `\u98ce\u9669\u7ea6\u675f`\uff1a\u4e8c\u6b21\u7ea6\u675f\uff0c\u7528\u4ee5\u7ea6\u675f\u7ec4\u5408\u7684\u98ce\u9669\n",
      "* `\u8ddf\u8e2a\u8bef\u5dee\u7ea6\u675f`\uff1a\u4e8c\u6b21\u7ea6\u675f\uff0c\u7528\u4ee5\u63a7\u5236\u7ec4\u5408\u7684\u8ddf\u8e2a\u8bef\u5dee\uff0c\u4e0e\u98ce\u9669\u7ea6\u675f\u7684\u533a\u522b\u5728\u4e8e\u8fd9\u91cc\u4f7f\u7528\u7684\u662f\u4e3b\u52a8\u6743\u91cd\n",
      "\n",
      "# **2. API**\n",
      "---\n",
      "**\u521b\u5efa\u4e00\u4e2a\u4f18\u5316\u5668\u5bf9\u8c61**\n",
      "**UqerOptimizer(signal, construct_date, risk_model='short', benchmark_str='ZZ500', \\*\\*kwargs)**\n",
      "\u91ca\u4e49\uff1a\u521b\u5efa\u4e00\u4e2a\u4f18\u5316\u5668\u5bf9\u8c61\n",
      "\u53c2\u6570\uff1a\n",
      "* signal(Series\uff0cdict\uff0clist\uff0cnumpy.ndarray)\uff1a\u4fe1\u53f7\uff0c\u53ef\u4ee5\u662f\u9884\u671f\u6536\u76ca\u7387\u3002\u5982\u679c\u4e3aSeries\uff0c\u90a3\u4e48\u7d22\u5f15\u4e3astr\u683c\u5f0f\u7684secID\uff0c\u503c\u4e3a\u8be5\u671f\u4fe1\u53f7\u503c\uff1b\u5982\u679c\u4e3adict\uff0c\u90a3\u4e48\u952e\u4e3astr\u683c\u5f0f\u7684secID\uff0c\u503c\u4e3a\u8be5\u671f\u4fe1\u53f7\u503c\n",
      "* construct_date(str)\uff1a\u7ec4\u5408\u6784\u5efa\u7684\u65e5\u671f\uff0c\u5408\u7406\u7684\u683c\u5f0f\u4e3a'YYYYMMDD'\n",
      "* risk_model(str)\uff1a\u98ce\u9669\u6a21\u578b\u9884\u6d4b\u5468\u671f\uff0c\u53ef\u9009\u7684\u53c2\u6570\u4e3a'day'\uff0c'short'\uff0c'long'\uff0c\u9ed8\u8ba4\u4e3a'short'\n",
      "* benchmark_str(str)\uff1a\u6307\u5b9a\u57fa\u51c6\uff0c\u9ed8\u8ba4\u4e3a\u4e2d\u8bc1500\u6307\u6570\uff0c\u5141\u8bb8\u7684\u503c\u5305\u62ec\uff0c'ZZ500', 'HS300', 'SH50', 'SH180', 'SZZS'\n",
      "* assets(Series\uff0cdict\u6216\u8005list)\uff1a\u7528\u6765\u5bfc\u5165\u4e0a\u671f\u7684\u6301\u4ed3\u6743\u91cd\uff0c\u4e3b\u8981\u7528\u4e8e\u6362\u624b\u7387\u7ea6\u675f\u5f53\u4e2d\u3002\u5982\u679c\u4e3aSeries\uff0c\u90a3\u4e48\u7d22\u5f15\u4e3a\u80a1\u7968\u540d\u79f0\uff0c\u503c\u4e3a\u4e0a\u671f\u6743\u91cd\uff1b\u5982\u679c\u4e3adict\uff0c\u90a3\u4e48\u952e\u4e3a\u80a1\u7968\u540d\u79f0\uff0c\u503c\u4e3a\u4e0a\u671f\u6743\u91cd\uff1b\n",
      "\n",
      "**UqerOptimizer\u7684\u76f8\u5173\u5c5e\u6027**\n",
      "* assets(DataFrame)\uff1a\u4e00\u4e2aindex\u4e3a\u80a1\u7968\u4ee3\u7801\uff0c\u5217\u4e3a'init_weights','min','max'\u7684DataFrame\uff0c\u7528\u6765\u8bb0\u5f55\u521d\u59cb\u6743\u91cd\uff0c\u548c\u6743\u91cd\u4e0a\u4e0b\u9650\u7ea6\u675f\u3002\n",
      "* optimal(bool)\uff1a\u8bb0\u5f55\u7ec4\u5408\u4f18\u5316\u7684\u72b6\u6001\uff0c\u9ed8\u8ba4\u4e3aFalse\uff0c\u4f18\u5316\u6210\u529f\u5219\u4e3aTrue\n",
      "* construct_date\uff1a\u8bb0\u5f55\u7ec4\u5408\u6784\u5efa\u7684\u65e5\u671f\n",
      "* risk_model_type(str)\uff1a\u7528\u6765\u8bb0\u5f55\u98ce\u9669\u6a21\u578b\u5b58\u50a8\u7684\u7c7b\u578b\n",
      "* custom_constraints(dict)\uff1a\u7528\u6765\u8bb0\u5f55\u7528\u6237\u4f20\u5165\u7684\u81ea\u5b9a\u4e49\u7ea6\u675f\uff0c\u5982\u679c\u4e0d\u4f20\uff0c\u5219\u4e3aNone\n",
      "\n",
      "**UqerOptimizer\u7684\u76f8\u5173\u65b9\u6cd5**\n",
      "**add_constraints(name, \\*\\*kwargs)**\n",
      "\u91ca\u4e49\uff1a\u6dfb\u52a0\u7ea6\u675f\n",
      "\u7531\u4e8e\u540e\u9762\u662f\u53ef\u53d8\u53c2\u6570\uff0c\u6211\u4eec\u5927\u6982\u4ecb\u7ecd\u8be5\u51fd\u6570\u652f\u6301\u7684\u6dfb\u52a0\u7ea6\u675f\u7684\u65b9\u5f0f\n",
      "1. `\u6dfb\u52a0\u6362\u624b\u7387\u7ea6\u675f`\uff1aadd_constraint(turnover_target)\n",
      "    * turnover_target(float)\uff1a\u6307\u5b9a\u7ec4\u5408\u7684\u6362\u624b\u7387\u4e0a\u9650\n",
      "2. `\u6dfb\u52a0\u4e2a\u80a1\u6743\u91cd\u7ea6\u675f`\uff1aadd_constraint(min_weights, max_weights, default_min_weight, default_max_weight)\n",
      "    * min_weights(pd.Series\uff0c dict)\uff1a\u5982\u679c\u4e3aSeries\uff0cindex\u4e3a\u80a1\u7968\uff0c\u503c\u4e3a\u6743\u91cd\uff0c\u5982\u679c\u4e3adict\uff0c\u952e\u4e3astr\u683c\u5f0f\u7684ticker\uff0c\u503c\u4e3a\u6743\u91cd\uff0c\u7528\u6765\u6307\u5b9aindex\u6240\u5305\u542b\u7684\u80a1\u7968\u7684\u6743\u91cd\u4e0b\u9650\uff0c\u53ef\u4e0d\u8f93\u3002\n",
      "    * max_weights(pd.Series\uff0c dict)\uff1a\u5982\u679c\u4e3aSeries\uff0cindex\u4e3a\u80a1\u7968\uff0c\u503c\u4e3a\u6743\u91cd\uff0c\u5982\u679c\u4e3adict\uff0c\u952e\u4e3astr\u683c\u5f0f\u7684ticker\uff0c\u503c\u4e3a\u6743\u91cd\uff0c\u7528\u6765\u6307\u5b9aindex\u6240\u5305\u542b\u7684\u80a1\u7968\u7684\u6743\u91cd\u4e0a\u9650\uff0c\u53ef\u4e0d\u8f93\u3002\n",
      "    * default_min_weight(float)\uff1a\u7528\u6765\u6307\u5b9a\u7ec4\u5408\u4e2a\u80a1\u9ed8\u8ba4\u7684\u6743\u91cd\u4e0b\u9650\uff0c\u4f18\u5148\u7ea7\u4f4e\u4e8emin_weight\n",
      "    * default_max_weight(float)\uff1a\u7528\u6765\u6307\u5b9a\u7ec4\u5408\u4e2a\u80a1\u9ed8\u8ba4\u7684\u6743\u91cd\u4e0a\u9650\uff0c\u4f18\u5148\u7ea7\u4f4e\u4e8emax_weight\n",
      "3. `\u6dfb\u52a0\u98ce\u683c\u56e0\u5b50\u4e3b\u52a8\u66b4\u9732\u7ea6\u675f`\uff1aadd_constraint(spec_style)\n",
      "    * style_value(dict): \u7528\u6765\u8bbe\u7f6e\u98ce\u683c\u56e0\u5b50\u7ea6\u675f\uff0c\u952e\u4e3a\u98ce\u683c\u56e0\u5b50\u7684\u540d\u79f0\uff0c\u503c\u4e3a\u76ee\u6807\u7684\u4e3b\u52a8\u66b4\u9732\u503c(\u57fa\u51c6\u5728\u521b\u5efa\u4f18\u5316\u5668\u5bf9\u8c61\u65f6\u6307\u5b9a)\u3002\u98ce\u683c\u56e0\u5b50\u53ea\u652f\u6301\u98ce\u9669\u6a21\u578b\u5f53\u4e2d\u7684\u98ce\u683c\u56e0\u5b50\uff1aBETA\uff0c MOMENTUM\uff0c SIZE\uff0c EARNYILD\uff0c RESVOL\uff0c GROWTH\uff0c BTOP\uff0c LEVERAGE\uff0c LIQUIDTY\uff0c SIZENL\u3002\u98ce\u683c\u7ea6\u675f\u7684\u503c\u5408\u7406\u7684\u503c\u5e94\u5728-3\u52303\u4e4b\u95f4\uff0c\u5177\u4f53\u8981\u770buniverse\u3002\n",
      "4. `\u6dfb\u52a0\u884c\u4e1a\u4e2d\u6027\u7ea6\u675f`\uff1aadd_constraint(is_industry_neutralize)\n",
      "    * is_industry_neutralize(bool):\u662f\u5426\u884c\u4e1a\u4e2d\u6027\n",
      "5. `\u81ea\u5b9a\u4e49\u884c\u4e1a\u7ea6\u675f`\uff1aadd_constraint(spec_indu)\n",
      "    * spec_indu(Series, dict):\u5141\u8bb8\u7528\u6237\u81ea\u5df1\u4f20\u5165\u884c\u4e1a\u914d\u7f6e\u65b9\u6848\uff0c\u952e\u503c\u4e3a\u884c\u4e1a\u540d\u79f0\uff0cvalue\u4e3a\u7528\u6237\u81ea\u5df1\u914d\u7f6e\u7684\u884c\u4e1a\u6743\u91cd, \u5982\u679c\u4e3a\u5b57\u5178\uff0c\u5219index\u4e3asecID\u3002\u6ce8\u610f\u884c\u4e1a\u4e3a\u7533\u4e07\u4e00\u7ea7\u884c\u4e1a\u5206\u7c7b\uff0c\u540d\u79f0\u4e3a\u4e2d\u6587\uff0c\u53ef\u4ee5\u901a\u8fc7DataAPI\u4e2d\u7684\u884c\u4e1a\u5206\u7c7b\u83b7\u5f97\n",
      "5. `\u6dfb\u52a0\u98ce\u9669\u7ea6\u675f`\uff1aadd_constraint(risk)\n",
      "\t* risk(float):\u5408\u7406\u7684\u503c\u5e94\u5927\u4e8e0\n",
      "6. `\u6dfb\u52a0\u8ddf\u8e2a\u8bef\u5dee\u7ea6\u675f`\uff1aadd_constraint(tracking_error)\n",
      "\t* tracking_error(float):\u5408\u7406\u7684\u503c\u5e94\u5927\u4e8e0\n",
      "7. `\u6dfb\u52a0\u81ea\u5b9a\u4e49\u7ea6\u675f`\uff1aadd_constraint(custom_constraints)\n",
      "\t* custom_constraints(dict):\u5b57\u5178\uff0c\u5305\u62ec\u4e09\u4e2a\u952e\uff0c\n",
      "\t\t* 'data'\uff0c\u8868\u793a\u4f20\u5165\u7684\u7ea6\u675f\u77e9\u9635\uff0c\u4e0d\u5141\u8bb8\u6709\u7f3a\u5931\u503c\uff0c\u800c\u4e14\u5e94\u8be5\u786e\u4fddsignal\u5f53\u4e2d\u6bcf\u4e2a\u80a1\u7968\u90fd\u6709\u503c\uff0c\u683c\u5f0f\u4e3aDataFrame\u6216Series\uff0c\u5176\u4e2d\uff0cindex\u4e3a\u80a1\u7968\u7684secID\n",
      "\t\t* 'upper'\uff0clist\uff0cnp.array\uff0c\u8bb0\u5f55\u7ea6\u675f\u7684\u4e0a\u9650\uff0c\u957f\u5ea6\u4e0edata\u7684\u5217\u957f\u4e00\u81f4\uff0c\u6ce8\u610f\uff0c\u5373\u4f7f\u81ea\u5b9a\u4e49\u7ea6\u675f\u53ea\u6709\u4e00\u4e2a\uff0c\u4e5f\u70e6\u8bf7\u5199\u5728\u5217\u8868\u5f53\u4e2d\n",
      "\t\t* 'lower'\uff0clist\uff0cnp.array\uff0c\u8bb0\u5f55\u7ea6\u675f\u7684\u4e0a\u9650\uff0c\u5176\u4ed6\u540c'upper'\n",
      "\n",
      "`\u53e6\u4e00\u79cd\u65b9\u5f0f`:add_constraint(is_industry_neutralize, turnover_target, max_weights, min_weights, default_max_weight, default_min_weight, spec_style, spec_industry,  tracking_error, risk, custom_constraints)\u3002\n",
      "`\u6ce8\u610f\uff1a\u4e24\u79cd\u65b9\u5f0f\u5fc5\u987b\u8f93\u5165\u53c2\u6570\u540d`\n",
      "\n",
      "\n",
      "**solve()**\n",
      "\u91ca\u4e49\uff1a\u6c42\u89e3\u4f18\u5316\u95ee\u9898\n",
      "\u4f1a\u5728assets\u5c5e\u6027\u5f53\u4e2d\u52a0'optimal_weights'\u4e00\u5217\uff0c\u5982\u679c\u6c42\u89e3\u6210\u529f\uff0c\u5219\u8be5\u5217\u4e3a\u4f18\u5316\u540e\u7684\u6743\u91cd\uff0c\u5426\u5219\uff0c\u4ecd\u7136\u8fd4\u56de\u4e0a\u671f\u6743\u91cd\u3002\n",
      "\n",
      "## **3. \u793a\u4f8b**\n",
      "---\n",
      "\u8fd9\u91cc\uff0c\u6211\u4eec\u5177\u4f53\u7ed9\u51fa\u4e00\u4e2a\u4f8b\u5b50\uff0c\u7528\u6765\u4ecb\u7ecd\u6211\u4eec\u4f18\u5316\u5668\u7684\u5177\u4f53\u7684\u7528\u6cd5\n",
      "### **`\u4f8b`**\uff1a"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "0885B6899DFF4EC8B7AE4BBAF47553C3",
     "input": [
      "import quartz_extensions.Optimizer.optimize as opt\n",
      "\n",
      "# \u83b7\u53d6\u4fe1\u53f7\n",
      "date = '20160930'\n",
      "data = DataAPI.MktStockFactorsOneDayProGet(tradeDate=date,\n",
      "                                        secID=set_universe('HS300', date),\n",
      "                                        field=u\"secID,tradeDate,OperatingProfitPS\", pandas=\"1\")\n",
      "signal = (data.pivot(index='tradeDate', columns='secID', values='OperatingProfitPS').dropna(axis=1)).iloc[0, :]\n",
      "signal = standardize(neutralize(winsorize(signal), date)).dropna()\n",
      "\n",
      "# \u521b\u5efa\u4f18\u5316\u5668\u5bf9\u8c61\n",
      "pspec = opt.UqerOptimizer(signal, date, benchmark_str='HS300')\n",
      "# \u6dfb\u52a0\u7ea6\u675f\n",
      "# \u4e2a\u80a1\u4e0a\u4e0b\u9650\u7ea6\u675f\n",
      "pspec.add_constraint(default_min_weight=0., default_max_weight=0.05)\n",
      "# \u884c\u4e1a\u4e2d\u6027\n",
      "pspec.add_constraint(is_industry_neutralize=True)\n",
      "\n",
      "# \u98ce\u683c\u7ea6\u675f\n",
      "pspec.add_constraint(spec_style={'SIZE':-0.05})\n",
      "pspec.solve()\n",
      "pspec.optimal"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "True"
       ]
      }
     ]
    },
    {
     "cell_type": "markdown",
     "id": "4A8E426CE2EF43EC8E02C201D164A477",
     "metadata": {},
     "source": [
      "`\u6216\u8005\u53ef\u4ee5\u8fd9\u6837`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "A9C8543C2044419C883D669EADA45488",
     "input": [
      "# \u521b\u5efa\u4f18\u5316\u5668\u5bf9\u8c61\n",
      "pspec = opt.UqerOptimizer(signal, date, benchmark_str='HS300')\n",
      "pspec.add_constraint(default_min_weight=0., default_max_weight=0.05, is_industry_neutralize=True, spec_style={'SIZE':-0.05})\n",
      "pspec.solve()\n",
      "pspec.optimal"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "True"
       ]
      }
     ]
    },
    {
     "cell_type": "markdown",
     "id": "80C9990DE2AB49E1969558D28340C9CD",
     "metadata": {},
     "source": [
      "**\u4f18\u5316\u540e\u7684\u7ed3\u679c**\n",
      "--\n",
      "\u4f18\u5316\u540e\u7684\u7ed3\u679c\u5b58\u5728assets\u8fd9\u4e2a\u5c5e\u6027\u5f53\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u5927\u4f53\u4e0a\u5730\u770b\u4e00\u90e8\u5206\u80a1\u7968\u7684\u7ed3\u679c"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "0E08EEB652314497A52900D50928371F",
     "input": [
      "weights = pspec.assets[pspec.assets.optimal_weights > 0.00001]\n",
      "weights.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>init_weights</th>\n",
        "      <th>max</th>\n",
        "      <th>min</th>\n",
        "      <th>optimal_weights</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>secID</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>000002.XSHE</th>\n",
        "      <td>0.003333</td>\n",
        "      <td>0.05</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.011507</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>000009.XSHE</th>\n",
        "      <td>0.003333</td>\n",
        "      <td>0.05</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.006025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>000039.XSHE</th>\n",
        "      <td>0.003333</td>\n",
        "      <td>0.05</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.022808</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>000538.XSHE</th>\n",
        "      <td>0.003333</td>\n",
        "      <td>0.05</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.004430</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>000651.XSHE</th>\n",
        "      <td>0.003333</td>\n",
        "      <td>0.05</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.031614</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "             init_weights   max  min  optimal_weights\n",
        "secID                                                \n",
        "000002.XSHE      0.003333  0.05  0.0         0.011507\n",
        "000009.XSHE      0.003333  0.05  0.0         0.006025\n",
        "000039.XSHE      0.003333  0.05  0.0         0.022808\n",
        "000538.XSHE      0.003333  0.05  0.0         0.004430\n",
        "000651.XSHE      0.003333  0.05  0.0         0.031614"
       ]
      }
     ]
    },
    {
     "cell_type": "markdown",
     "id": "81DEFD85E0BA4FA191D0A0D34B3641C2",
     "metadata": {},
     "source": [
      "\u8fd9\u91cc\uff0c\u6211\u4eec\u6ca1\u6709\u4f20\u5165\u4e0a\u4e00\u671f\u6743\u91cd\uff0c\u6240\u4ee5\u521d\u59cb\u5316\u4e86\u4e00\u4e2a\u7b49\u6743\u7ec4\u5408\uff0c\u5982\u679c\u8981\u6dfb\u52a0\u6362\u624b\u7387\u7ea6\u675f\uff0c\u90a3\u4e48\u6211\u4eec\u8fd8\u9700\u8981\u5728\u521b\u5efa\u4f18\u5316\u5668\u5bf9\u8c61\u65f6\u4f20\u5165\u4e0a\u4e00\u671f\u7684\u6743\u91cd\u3002"
     ]
    },
    {
     "cell_type": "markdown",
     "id": "7BA903A28B3243DEB054753754C1C2A7",
     "metadata": {},
     "source": [
      "**\u6743\u91cd\u5206\u5e03\u56fe**\n",
      "--\n",
      "\u8fd9\u91cc\u5c55\u793a\u4e86\u6743\u91cd\u5927\u4e8e0.00001\u7684\u80a1\u7968\u7684\u6743\u91cd\u7684\u5206\u5e03"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "ACBFFFC66EDC4856BFDFD825AF524858",
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import seaborn as sns\n",
      "from CAL.PyCAL import *\n",
      "sns.set_style('white')\n",
      "fig = plt.figure(figsize=(16,5))\n",
      "ax = fig.add_subplot(111)\n",
      "ax = weights.optimal_weights.plot(kind='bar', ax=ax)\n",
      "ax.set_title(u'\u90e8\u5206\u80a1\u7968\u6743\u91cd\u5206\u5e03', fontproperties=font, fontsize=16)\n",
      "ax.set_xlabel(u'\u80a1\u7968\u4ee3\u7801', fontproperties=font, fontsize=13)\n",
      "ax.set_ylabel(u'\u6743\u91cd', fontproperties=font, fontsize=13)\n",
      "ax.grid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "data": {
        "image/png": 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q0tq1azVz5kw9+eSTkk4eXV60aJFWrVql9957T5s2bdKWLVs8eNoAAAAAAKuq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\n8+ZNXFyqp7Bjx46xfft2ALp160ZkZKT4xV+vfmvEEL/4bT2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        "text/plain": "<matplotlib.figure.Figure at 0x7f2b590>"
       },
       "metadata": {},
       "output_type": "display_data",
       "png": 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q0tq1azVz5kw9+eSTkk4eXV60aJFWrVql9957T5s2bdKWLVs8eNoAAAAAAKuq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\n",
       "source": "display",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2b590>"
       ]
      }
     ]
    },
    {
     "cell_type": "markdown",
     "id": "63EF1A394C33403C804F6972769C715A",
     "metadata": {},
     "source": [
      "**\u884c\u4e1a\u5206\u5e03\u6bd4\u8f83**\n",
      "--\n",
      "\u8fd9\u91cc\u6211\u4eec\u505a\u7684\u662f\u884c\u4e1a\u4e2d\u6027\uff0c\u6240\u4ee5\u4f18\u5316\u540e\u7ec4\u5408\u7684\u884c\u4e1a\u6743\u91cd\u5e94\u4e0e\u57fa\u51c6\u7684\u884c\u4e1a\u6743\u91cd\u4e00\u81f4"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "CE16A95E4E034479976E026B04F6F00E",
     "input": [
      "# \u83b7\u5f97\u57fa\u51c6\u6210\u5206\u80a1\u884c\u4e1a\u5206\u7c7b\n",
      "equ_indu = DataAPI.EquIndustryGet(industryVersionCD=u\"010303\", secID=set_universe('HS300', '20160930'), intoDate='20160930', field=u\"secID,industryName1\", pandas=\"1\")\n",
      "benchmark_equ_weight = DataAPI.IdxCloseWeightGet(ticker='000300', beginDate='20160930', endDate='20160930', field=u\"consID,weight\",pandas=\"1\")\n",
      "benchmark_equ_weight.rename(columns={'consID': 'secID', 'weight':u'\u57fa\u51c6\u884c\u4e1a\u6743\u91cd'}, inplace=True)\n",
      "\n",
      "# \u83b7\u5f97\u57fa\u51c6\u884c\u4e1a\u6743\u91cd\n",
      "benchmark_indu_weight =equ_indu.merge(benchmark_equ_weight, on='secID', how='inner').groupby(by='industryName1').sum()/100"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "DB04334F58064F1580A23EE2DB4CE157",
     "input": [
      "equ_indu_ = DataAPI.EquIndustryGet(industryVersionCD=u\"010303\", secID=list(pspec.assets.index), intoDate='20160930', field=u\"secID,industryName1\", pandas=\"1\")\n",
      "pspec_indu_weight=equ_indu_.merge(pspec.assets[['optimal_weights']], left_on='secID', right_index=True, how='inner').groupby(by='industryName1').sum()\n",
      "pspec_indu_weight.rename(columns={'optimal_weights':u'\u7ec4\u5408\u884c\u4e1a\u6743\u91cd'}, inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "15F23A6FCED6481BACB19F78634CD3C2",
     "input": [
      "data = pspec_indu_weight.merge(benchmark_indu_weight, left_index=True, right_index=True, how='inner')\n",
      "fig = plt.figure(figsize=(16,5))\n",
      "ax = fig.add_subplot(111)\n",
      "ax = data.plot(kind='bar', ax=ax)\n",
      "labels = [label.decode(\"utf-8\") for label in data.index.values] \n",
      "ax.set_xticklabels(labels, fontproperties=font, fontsize=13)\n",
      "ax.legend(prop=font, loc='best')\n",
      "ax.set_xlabel(u'\u884c\u4e1a\u540d\u79f0', fontproperties=font, fontsize=15)\n",
      "ax.set_ylabel(u'\u6743\u91cd', fontproperties=font, fontsize=15)\n",
      "ax.grid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "data": {
        "image/png": 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AAAAAAJWhQoUXAAAAAIDrDYUXAAAAAGBIFF4AAAAAgCFReAEAAAAAhkThBQAA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\nAAAASUVORK5CYII=\n",
        "text/plain": "<matplotlib.figure.Figure at 0x74fd710>"
       },
       "metadata": {},
       "output_type": "display_data",
       "png": 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AAAAAAJWhQoUXAAAAAIDrDYUXAAAAAGBIFF4AAAAAgCFReAEAAAAAhkThBQAA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\nAAAASUVORK5CYII=\n",
       "source": "display",
       "text": [
        "<matplotlib.figure.Figure at 0x74fd710>"
       ]
      }
     ]
    },
    {
     "cell_type": "markdown",
     "id": "5456D5FF77E049DE823FDC84693529D1",
     "metadata": {},
     "source": [
      "\u53ef\u4ee5\u770b\u5230\uff0c\u6dfb\u52a0\u884c\u4e1a\u4e2d\u6027\u7ea6\u675f\u540e\uff0c\u4f18\u5316\u5668\u80fd\u591f\u5c06\u7ec4\u5408\u7684\u884c\u4e1a\u6743\u91cd\u548c\u57fa\u51c6\u7684\u884c\u4e1a\u6743\u91cd\u7684\u5206\u5e03\u53d8\u5f97\u57fa\u672c\u4e00\u6837\u3002"
     ]
    },
    {
     "cell_type": "markdown",
     "id": "12203EA4FE9F489BA9FBDFF8AC35B2F5",
     "metadata": {},
     "source": [
      "# \u7248\u672c\u5386\u53f2\n",
      "\n",
      "V1.0\uff0c2017-06-15\uff0c\u6587\u7ae0\u4e0a\u7ebf\n",
      "V1.1\uff0c2017-12-26\uff0c\u589e\u52a0\u8ddf\u8e2a\u8bef\u5dee\u7ea6\u675f\n",
      "V1.2\uff0c2018-02-26\uff0c\u8c03\u6574\u6307\u6570\u6210\u4efd\u80a1\u63a5\u53e3\u4e3a DataAPI.IdxCloseWeightGet"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}